Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

CARRE: Personalized patient empowerment and shared decision support

145 views

Published on

Eleni Kaldoudi
CARRE: Personalized patient empowerment and shared decision support for cardiorenal disease and comorbidities
Presentation & Demo
HealthInf 2016: 9th International Conference on Health Informatics, part of BIOSTEC, Rome, Italy, 21-23 February, 2016

Published in: Science
  • Be the first to comment

  • Be the first to like this

CARRE: Personalized patient empowerment and shared decision support

  1. 1. HealthInf 2016: 9th International Conference on Health Informatics BIOSTEC, Rome, Italy, 21-23 February, 2016 CARRE Personalized patient empowerment and shared decision support for cardiorenal disease and comorbidities Eleni Kaldoudi CARRE Coordinator Associate Professor School of Medicine, Democritus University of Thrace, Greece
  2. 2. HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi motivation  significant increase in the prevalence and incidence of chronic disease  ½ of all chronic patients present comorbidities  the chronic patient is mostly an outpatient  needs to care for herself at home  mainly away from continuous professional care  while trying to lead a normal life prevent detect manage
  3. 3. HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi medical domain chronic cardiorenal disease and comorbidities  simultaneous (causal) dysfunction of kidney and heart  diabetes and/or hypertension common underlying causes  a number of other serious comorbidities often present nephrogenic anemia, renal osteodystrophy, malnutrition, blindness, neuropathy, severe atherosclerosis, cardiovascular episodes, and eventually end-stage renal disease and/or heart failure, and death  deterioration to end stage renal/heart disease is life threatening, irreversible and expensive to manage
  4. 4. HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi cardiorenal disease & comorbidities some numbers…  hypertension  1/3 of adults (US 2008)  diabetes  8% of overall population  chronic kidney disease  9-16% of overall population  44% of chronic kidney disease is due to diabetes  86% of chronic kidney disease has at least 1 comorbidity  most patients with chronic kidney disease develop cardiovascular disease  chronic heart failure  1-2% of total healthcare costs  end-stage renal disease (dialysis)  >2% of total healthcare costs
  5. 5. HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi CARRE Cardiorenal comorbidity management via empowerment and shared informed decision FP7-ICT-2013-611140 consortium: 6 partners from 4 EU countries coordinator: Eleni Kaldoudi (DUTH) duration: Nov 2013 – Oct 2016 budget: 3,210,470€ http://carre-project.eu/ Democritus Univ. of Thrace DUTH, GR The Open University, UK Univ. of Bedfordshire, UK Vilnius Univ. Hospital, LT Kaunas Univ., LT Industrial Research Institute for Automation & Measurements, PL
  6. 6. HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi CARRE approach foster understanding of comorbid condition calculate informed comorbidity progression compile personalized empowerment services support shared informed decision and integrated management
  7. 7. HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi medical evidence aggregation evidence based medical literature Educational resources … social media personal health information quantified self weight physical activity blood pressure glucose CARRE approach private public data harvesting & interlinking LOD comorbidity model visualization (generic and personalized) patient empowerment & decision support services
  8. 8. HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi risk factor as a central concept disorder 1 (as a risk factor) disorder 2 (as a probable consequence) leads to under certain conditions with a probability x E. Kaldoudi, et al. CARRE D.2.1, 2014, www.carre-project.eu risk factors are reported in medical literature (top level evidence: systematic reviews with meta-analysis)
  9. 9. characterizes is a value of type of risk element biomedical ratio value confidence intervalratio type adjustment for risk factor as a central concept E. Kaldoudi, et al. CARRE D.2.2, 2014 risk element observable observable condition satisfies observable condition 1…N determines risk evidence 1…N has risk ratio risk ratio evidence source has evidence source source risk element target risk element causes, is an issue in, … risk evidence demographic genetic behavioural intervention environmental is a value of 1…N measures the state of risk element condition disorder A. Third, E. Kaldoudi, G. Gotsis, S. Roumeliotis, K. Pafili, J. Domingue, Capturing Scientific Knowledge on Medical Risk Factors, K-CAP2015, ACM, NY, USA, Oct. 7-10, 2015
  10. 10. CARRE risk factor ontology conceptual model G. Gkotis, A. Third, CARRE D.2.4, 2014, www.carre-project.eu
  11. 11. CARRE risk factor ontology CARRE ontology published in NCBO BioPortal http://bioportal.bioontology.org/ontologies/CARRE G. Gkotis, A. Third, CARRE D.2.4, 2014, www.carre-project.eu
  12. 12. risk factor identification methodology search ground knowledge to identify major risk factors (guidelines and their literature: KDIGO, KDOQI, ACC/AHA, NICE, ESC, EASD, ADA) if result found include all risk evidences from the most recent yes search PubMed: condition A AND condition B (limited to systematic reviews with metaanalyses identify major risk factors (keywords) search PubMed: condition A AND condition B noif result found include relevant risk evidence from latest and highest level yes search again for next update (1 year) no E. Kaldoudi, et al. CARRE D.2.2, 2014
  13. 13. HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi some of the major related conditions 1. Acute kidney injury 2. Acute myocardial infarction 3. Age 4. Albuminuria 5. Anaemia 6. Angina pectoris 7. Asthma 8. Atrial fibrillation 9. Chronic kidney disease 10. Chronic obstructive pulmonary disease 11. Cholelithiasis 12. Colorectal Cancer 13. Coronary and carotid revascularisation 14. Death 15. Depression 16. Diabetes 17. Diabetic nephropathy 18. Drugs 19. Dyslipidemia 20. Family history 21. Heart Failure 22. Hyperkalemia 23. Hypertension 24. Hyperuricemia 25. Hypoglycaemia 26. Ischemic heart disease 27. Ischemic stroke 28. Left ventricular hypertrophy 29. Obesity 30. Obstructive Sleep Apnoea 31. Myocardial infarction 32. Osteoarthritis 33. Pancreatic Cancer 34. Peripheral Arterial Disease 35. Physical activity 36. Smoking 37. …
  14. 14. HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi medical evidence aggregator https://www.carre-project.eu/innovation/medical-evidence-aggregator/ E. Liu, et al. CARRE D.3.4, 2015 sentence splitter tokenizer (GATE &Jape) part-of-speech tagging dependency parsing semantic role labelling
  15. 15. HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi medical evidence aggregator https://www.carre-project.eu/innovation/medical-evidence-aggregator/ E. Liu, et al. CARRE D.3.4, 2015
  16. 16. HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi obesity diabetescauses when 23BMI34 risk ratio = 1.61 obesity hypertensioncauses when 99.4  Waist Circumference  106.2 AND sex=male risk ratio = 2.50 obesity heart failurecauses when 25  BMI  30 AND sex=female risk ratio = 2.50
  17. 17. HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi hypertension chronic renal diseasecauses when systolic BB  140mmHg AND/OR diastolic BB  90 mmHg risk ratio = 2.00 smoking chronic renal disease is an issue in when smoking status = current AND sex=male risk ratio = 2.40 so far… 253 major risk associations (or evidences) identified in medical literature (which involve 53 health conditions and 82 related observables) as included in the CARRE risk factor database and predictive model
  18. 18. HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi http://entry.carre-project.eu/
  19. 19. http://entry.carre-project.eu/
  20. 20. http://entry.carre-project.eu/
  21. 21. http://entry.carre-project.eu/
  22. 22. http://entry.carre-project.eu/
  23. 23. http://entry.carre-project.eu/
  24. 24. http://entry.carre-project.eu/
  25. 25. http://entry.carre-project.eu/
  26. 26. http://entry.carre-project.eu/
  27. 27. http://entry.carre-project.eu/
  28. 28. http://entry.carre-project.eu/
  29. 29. CARRE D.5.3, 2016 (in progress)
  30. 30. http://entry.carre-project.eu/
  31. 31. CARRE D.5.3, 2016 (in progress)
  32. 32. CARRE D.5.3, 2016 (in progress)
  33. 33. CARRE D.5.3, 2016 (in progress)
  34. 34. CARRE D.5.3, 2016 (in progress)
  35. 35. CARRE D.5.3, 2016 (in progress)
  36. 36. HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi medical evidence aggregation evidence based medical literature Educational resources … social media personal health information quantified self weight physical activity blood pressure glucose CARRE approach private public data harvesting & interlinking LOD comorbidity model visualization (generic and personalized) patient empowerment & decision support services
  37. 37. HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi personal data aggregators (WP3)  sensor aggregators  medical data aggregators from personal health record  manual entry system for personal medical data  intention extraction form web searches CARRE D.3..2 & D.3.3, 2015
  38. 38. HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi project site  innovation  breakthroughs https://www.carre-project.eu/innovation/breakthroughs/ CARRE D.3..2 & D.3.3, 2015
  39. 39. aggregator integration https://carre.kmi.open.ac.uk/devices/ CARRE D.3..2 & D.3.3, 2015
  40. 40. CARRE sensor innovation  multiparametric CARRE Weight Scale - body fluid balance monitoring, other parameters  ECG aggregator – atrial fibrillation detection  CARRE Wristwatch – heart arrhytmia detection, … Paliakaite B., Daukantas S., Sakalauskas A., Marozas V., "Estimation of pulse arrival time using impedance plethysmogram from body composition scales," in Sensors Applications Symposium (SAS), 2015 IEEE, pp.1-4, 13-15 April 2015 Paliakaitė B., Daukantas S., Marozas V., Assessment of Pulse Arrival Time for Arterial Stiffness Monitoring on Body Composition Scales, Computers in Biology and Medicine, submitted to special issue: Self-monitoring systems for personalized health-care and lifestyle surveillance, 05/10/2015. Petrenas, V. Marozas, L. Sörnmo, Low-complexity detection of atrial fibrillation in continuous long-term monitoring, Computers in Biology and Medicine, vol. 35, iss. 47, pp. 3365-3376, 2015. Stankevicius et al. „Photoplethysmography based system for atrial fibrillation detection during hemodialysis“, submited to Medicon 2016
  41. 41. HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi medical evidence aggregation evidence based medical literature Educational resources … social media personal health information quantified self weight physical activity blood pressure glucose CARRE approach private public data harvesting & interlinking LOD comorbidity model visualization (generic and personalized) patient empowerment & decision support services
  42. 42. HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi decision support services interlace personal data and medical evidence for personalized services to  plan  monitor  alert  educate CARRE D.6.2 & D.6.3, 2016 (in progress)
  43. 43. HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi educational aggregator https://edu.carre-project.eu/ Resource Retriever Query Generator Educational Object Harvester Medical knowledge data aggregator Backend Query Terms Extractor Educational Object Metadata Extractor Metadata Enrichment and Mapping to CARRE schema Expert Application Frontend – user interface Educational Object Rating Module Educational Metadata Sender CARRE Server public RDF Resource Rating Module educationalrepositories Local Storage of metadataResource Metadata Processing semanticweb CARRE D.3.4, 2015
  44. 44. HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi medical evidence aggregation evidence based medical literature Educational resources … social media personal health information quantified self weight physical activity blood pressure glucose CARRE approach private public data harvesting & interlinking LOD comorbidity model visualization (generic and personalized) patient empowerment & decision support services
  45. 45. HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi project site  innovation  breakthroughs https://www.carre-project.eu/innovation/breakthroughs/
  46. 46. HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi public RDF SPARQL endpoint a SPARQL query to retrieve RDF triples about educational objects A. Third et al, CARRE D.4.1 & D.4.2, 2015
  47. 47. HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi triples about educational objects
  48. 48. HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi restful API A. Third et al, CARRE D.4.1 & D.4.2, 2015
  49. 49. HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi evaluation framework & plan* CARRE system functions Human perspectives Context and EnvironmentExperts Patients Admins Structure aggregators and interfaces functioning changes to working conditions and practices; new skills, & abilities new skills, and abilities Process service operation correct & valid induced changes in function and satisfaction Induced changes in self-management & satisfaction Outcome service usable and reliable effectiveness perceived quality of care and life improving specific clinical parameters potential to improve the health status and quality of life 1: component testing 2: service testing & understanding 3: service evaluation * based on the model proposed by Cornford, T., Doukidis, G.I., and Forster, D., Int. J. Manag. Science, 22(5), 491-504, 1994
  50. 50. HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi evaluation  2 center randomized control trial  primary objectives  increase health literacy  increase level of patient empowerment (SUSTAINS instrument)  improve quality of life (SF-36 instrument)  reduce personal risk for cardiorenal disease and comorbidities  improve or prevent disease progression  improve lifestyle habits  limit no. or dose of necessary drugs  assess intervention acceptability study population for each pilot site (total = 160 patients) group 1 patients at risk of heart or renal disease (80 patients) CARRE intervention (40 patients) control group (40 patients) group 2 patients with heart or renal disease (80 patients) CARRE intervention (40 patients) control group (40 patients) primarysecondary (biomarkers & disease prevalence) (sensor readings) (dose of essential drugs) (clinical & laboratory parameters) CARRE D.7.4, 2016 (in progress)
  51. 51. what? CARRE EU FP7-ICT-2013-611140 3.2M, 2013-2016 DUTH, OU, BED, VULSK, KTU, PIAP why? cardiorenal disease chronic, common, dangerous, expensive, with many causing factors and complex progression how? quantified self medical evidence personal risk prediction personal decision support for the patient for the medical expert Processor Real time clock & Calendar Micro SD Card 4GB Electrocardiogram I, II, III leads TI front-end ADS1294DRL RA LL LA BlueTooth Power LA RA LL Body Composition / Weight Scale TI front-end AFE4300 LCD WiFi CARRE server sensor developments for the ICT expert Personalized patient empowerment and shared decision support for cardiorenal disease and comorbidities
  52. 52. thank you !!!
  53. 53. acknowledgment work funded under project CARRE co-funded by the European Commission under the Information and Communication Technologies (ICT) 7th Framework Programme Contract No. FP7-ICT-2013-611140 CARRE: Personalized patient empowerment and shared decision support for cardiorenal disease and comorbidities http://www.carre-project.eu/
  54. 54. HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi Contact Eleni Kaldoudi Associate Professor School of Medicine Democritus University of Thrace Dragana, Alexandroupoli 68100 Greece Tel: +302551030329 Tel: +30 6937124358 Email: kaldoudi@med.duth.gr Email: carre@med.duth.gr Cite as Eleni Kaldoudi CARRE: Personalized patient empowerment and shared decision support for cardiorenal disease and comorbidities Presentation & Demo HealthInf 2016: 9th International Conference on Health Informatics, part of BIOSTEC, Rome, Italy, 21-23 February, 2016

×